This paper presents a infeasible interior-point primal -dual affine scaling algorithm for linear programming. it is shown that the method is polynomial-time algorithm.
摘要本文对线性规划提出了一个不可行内点原始-对偶仿射尺度算法，并证明了算法是一个多项式时间算法。
In this paper, we consider an affine-scaling algorithm for the bound constrained optimization problem.
本文我们考虑求解边界约束优化问题的一个仿射尺度算法。
The algorithm can adapt to object recognition, invariant not only under rotation? Scaling? Translation, but under affine and projective transformation.
算法不仅能适应目标物体在旋转、缩放、平移变换下的不变性识别。而且能适应仿射及射影变换下的不变性识别。
We present an affine scaling trust region algorithm with interior back - tracking and subspace techniques for nonlinear optimizations subject to linear inequality constraints.
使用仿射变换内点回代技术的信赖域子空间算法解线性不等式约束的非线性优化问题。
We present an affine scaling trust region algorithm with interior back - tracking and subspace techniques for nonlinear optimizations subject to linear inequality constraints.
使用仿射变换内点回代技术的信赖域子空间算法解线性不等式约束的非线性优化问题。
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